Overview

Dataset statistics

Number of variables14
Number of observations642
Missing cells642
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory341.2 KiB
Average record size in memory544.2 B

Variable types

CAT7
NUM6
UNSUPPORTED1

Warnings

PARK has a high cardinality: 241 distinct values High cardinality
BUILDING NAME has a high cardinality: 94 distinct values High cardinality
LOCATION has a high cardinality: 639 distinct values High cardinality
Location 1 has a high cardinality: 639 distinct values High cardinality
DEMOLISHED has 642 (100.0%) missing values Missing
LOCATION is uniformly distributed Uniform
Location 1 is uniformly distributed Uniform
DEMOLISHED is an unsupported type, check if it needs cleaning or further analysis Unsupported
YEAR BUILT has 574 (89.4%) zeros Zeros

Reproduction

Analysis started2020-12-13 01:18:28.502065
Analysis finished2020-12-13 01:18:32.767235
Duration4.27 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

PARK
Categorical

HIGH CARDINALITY

Distinct241
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
LINCOLN (ABRAHAM)
73 
JACKSON (ANDREW)
 
30
GRANT (ULYSSES)
 
23
MARQUETTE (JACQUES)
 
18
HUMBOLDT (BARON VON)
 
13
Other values (236)
485 
ValueCountFrequency (%) 
LINCOLN (ABRAHAM)7311.4%
 
JACKSON (ANDREW)304.7%
 
GRANT (ULYSSES)233.6%
 
MARQUETTE (JACQUES)182.8%
 
HUMBOLDT (BARON VON)132.0%
 
WASHINGTON (GEORGE)132.0%
 
BURNHAM (DANIEL)132.0%
 
NORTHERLY ISLAND121.9%
 
COLUMBUS (CHRISTOPHER)101.6%
 
GARFIELD (JAMES)91.4%
 
CALUMET81.2%
 
WARREN (LAURENCE)81.2%
 
PORTAGE71.1%
 
RIVER60.9%
 
PALMER (POTTER)60.9%
 
MCKINLEY (WILLIAM)60.9%
 
FOSTER (J. FRANK)60.9%
 
RIIS (JACOB)60.9%
 
INDIAN BOUNDARY50.8%
 
HAMILTON (ALEXANDER)50.8%
 
WEST LAWN50.8%
 
DOUGLAS (STEPHEN)50.8%
 
LOYOLA50.8%
 
PULASKI (CASIMER)40.6%
 
JEFFERSON (THOMAS) MEML.40.6%
 
Other values (216)34253.3%
 
2020-12-12T20:18:32.861317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique140 ?
Unique (%)21.8%
2020-12-12T20:18:32.957400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length16
Mean length15.3894081
Min length5

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A9469.6%
 
E7347.4%
 
N7247.3%
 
R6967.0%
 
6656.7%
 
O5936.0%
 
L5916.0%
 
)4664.7%
 
(4654.7%
 
I4314.4%
 
S4254.3%
 
T3723.8%
 
H3553.6%
 
M3323.4%
 
C2872.9%
 
D2442.5%
 
U2352.4%
 
B2162.2%
 
G1992.0%
 
W1691.7%
 
J1221.2%
 
K1191.2%
 
Y1101.1%
 
P1061.1%
 
F590.6%
 
Other values (18)2192.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter818482.8%
 
Space Separator6656.7%
 
Close Punctuation4664.7%
 
Open Punctuation4654.7%
 
Other Punctuation600.6%
 
Decimal Number360.4%
 
Dash Punctuation4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A94611.6%
 
E7349.0%
 
N7248.8%
 
R6968.5%
 
O5937.2%
 
L5917.2%
 
I4315.3%
 
S4255.2%
 
T3724.5%
 
H3554.3%
 
M3324.1%
 
C2873.5%
 
D2443.0%
 
U2352.9%
 
B2162.6%
 
G1992.4%
 
W1692.1%
 
J1221.5%
 
K1191.5%
 
Y1101.3%
 
P1061.3%
 
F590.7%
 
V580.7%
 
Q430.5%
 
Z120.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
665100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(465100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)466100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.4575.0%
 
,58.3%
 
"46.7%
 
'46.7%
 
&23.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
21233.3%
 
51233.3%
 
1513.9%
 
738.3%
 
412.8%
 
012.8%
 
612.8%
 
812.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin818482.8%
 
Common169617.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A94611.6%
 
E7349.0%
 
N7248.8%
 
R6968.5%
 
O5937.2%
 
L5917.2%
 
I4315.3%
 
S4255.2%
 
T3724.5%
 
H3554.3%
 
M3324.1%
 
C2873.5%
 
D2443.0%
 
U2352.9%
 
B2162.6%
 
G1992.4%
 
W1692.1%
 
J1221.5%
 
K1191.5%
 
Y1101.3%
 
P1061.3%
 
F590.7%
 
V580.7%
 
Q430.5%
 
Z120.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
66539.2%
 
)46627.5%
 
(46527.4%
 
.452.7%
 
2120.7%
 
5120.7%
 
,50.3%
 
150.3%
 
"40.2%
 
-40.2%
 
'40.2%
 
730.2%
 
&20.1%
 
410.1%
 
010.1%
 
610.1%
 
810.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9880100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A9469.6%
 
E7347.4%
 
N7247.3%
 
R6967.0%
 
6656.7%
 
O5936.0%
 
L5916.0%
 
)4664.7%
 
(4654.7%
 
I4314.4%
 
S4254.3%
 
T3723.8%
 
H3553.6%
 
M3323.4%
 
C2872.9%
 
D2442.5%
 
U2352.4%
 
B2162.2%
 
G1992.0%
 
W1691.7%
 
J1221.2%
 
K1191.2%
 
Y1101.1%
 
P1061.1%
 
F590.6%
 
Other values (18)2192.2%
 

PARK NUMBER
Real number (ℝ≥0)

Distinct241
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.4065421
Minimum2
Maximum1268
Zeros0
Zeros (%)0.0%
Memory size5.1 KiB
2020-12-12T20:18:33.041972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q127
median128.5
Q3250.75
95-th percentile1048.8
Maximum1268
Range1266
Interquartile range (IQR)223.75

Descriptive statistics

Standard deviation319.9955706
Coefficient of variation (CV)1.267778434
Kurtosis2.193302379
Mean252.4065421
Median Absolute Deviation (MAD)104.5
Skewness1.835988972
Sum162045
Variance102397.1652
MonotocityNot monotonic
2020-12-12T20:18:33.135052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1007311.4%
 
19304.7%
 
24233.6%
 
10182.8%
 
219132.0%
 
27132.0%
 
21132.0%
 
34121.9%
 
209101.6%
 
20491.4%
 
1181.2%
 
42881.2%
 
14771.1%
 
2660.9%
 
18660.9%
 
1360.9%
 
12360.9%
 
2360.9%
 
24550.8%
 
21850.8%
 
16550.8%
 
950.8%
 
11550.8%
 
2940.6%
 
25140.6%
 
Other values (216)34253.3%
 
ValueCountFrequency (%) 
210.2%
 
310.2%
 
430.5%
 
510.2%
 
620.3%
 
730.5%
 
830.5%
 
950.8%
 
10182.8%
 
1181.2%
 
ValueCountFrequency (%) 
126810.2%
 
125540.6%
 
123910.2%
 
122010.2%
 
120410.2%
 
116420.3%
 
115910.2%
 
107420.3%
 
107310.2%
 
107210.2%
 

BUILDING TYPE
Categorical

Distinct35
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
POOL BUILDING
61 
COMFORT STATION
51 
UNIDENTIFIED
48 
MAINTENANCE
45 
FIELDHOUSE D2
43 
Other values (30)
394 
ValueCountFrequency (%) 
POOL BUILDING619.5%
 
COMFORT STATION517.9%
 
UNIDENTIFIED487.5%
 
MAINTENANCE457.0%
 
FIELDHOUSE D2436.7%
 
SHELTER385.9%
 
FIELDHOUSE A3365.6%
 
UTILITY STRUCTURE325.0%
 
ZOO BUILDING314.8%
 
FIELDHOUSE A1304.7%
 
FIELDHOUSE C223.4%
 
FIELDHOUSE A4203.1%
 
FIELDHOUSE A2172.6%
 
FIELDHOUSE D1172.6%
 
FIELDHOUSE JOINT172.6%
 
CONCESSION162.5%
 
MUSEUM152.3%
 
HARBOR BUILDING142.2%
 
FIELDHOUSE BH132.0%
 
GOLF BUILDING132.0%
 
FIELDHOUSE B1101.6%
 
HISTORIC91.4%
 
FIELDHOUSE B381.2%
 
OTHER60.9%
 
FIELDHOUSE B260.9%
 
Other values (10)243.7%
 
2020-12-12T20:18:33.235639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.5%
2020-12-12T20:18:33.316208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length13
Mean length12.28193146
Min length5

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I84510.7%
 
E82210.4%
 
O6758.6%
 
U5466.9%
 
L5206.6%
 
D5206.6%
 
T4595.8%
 
N4555.8%
 
4475.7%
 
S4315.5%
 
F3514.5%
 
H3254.1%
 
A2813.6%
 
R2122.7%
 
C2092.7%
 
B1772.2%
 
M1331.7%
 
G1321.7%
 
P670.8%
 
2660.8%
 
1570.7%
 
3440.6%
 
Y410.5%
 
Z310.4%
 
4200.3%
 
Other values (2)190.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter725192.0%
 
Space Separator4475.7%
 
Decimal Number1872.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I84511.7%
 
E82211.3%
 
O6759.3%
 
U5467.5%
 
L5207.2%
 
D5207.2%
 
T4596.3%
 
N4556.3%
 
S4315.9%
 
F3514.8%
 
H3254.5%
 
A2813.9%
 
R2122.9%
 
C2092.9%
 
B1772.4%
 
M1331.8%
 
G1321.8%
 
P670.9%
 
Y410.6%
 
Z310.4%
 
J170.2%
 
V2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
447100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
26635.3%
 
15730.5%
 
34423.5%
 
42010.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin725192.0%
 
Common6348.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I84511.7%
 
E82211.3%
 
O6759.3%
 
U5467.5%
 
L5207.2%
 
D5207.2%
 
T4596.3%
 
N4556.3%
 
S4315.9%
 
F3514.8%
 
H3254.5%
 
A2813.9%
 
R2122.9%
 
C2092.9%
 
B1772.4%
 
M1331.8%
 
G1321.8%
 
P670.9%
 
Y410.6%
 
Z310.4%
 
J170.2%
 
V2< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
44770.5%
 
26610.4%
 
1579.0%
 
3446.9%
 
4203.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7885100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I84510.7%
 
E82210.4%
 
O6758.6%
 
U5466.9%
 
L5206.6%
 
D5206.6%
 
T4595.8%
 
N4555.8%
 
4475.7%
 
S4315.5%
 
F3514.5%
 
H3254.1%
 
A2813.6%
 
R2122.7%
 
C2092.7%
 
B1772.2%
 
M1331.7%
 
G1321.7%
 
P670.8%
 
2660.8%
 
1570.7%
 
3440.6%
 
Y410.5%
 
Z310.4%
 
4200.3%
 
Other values (2)190.2%
 

BUILDING STATUS
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
ACTIVE
641 
INACTIVE
 
1
ValueCountFrequency (%) 
ACTIVE64199.8%
 
INACTIVE10.2%
 
2020-12-12T20:18:33.391273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-12-12T20:18:33.437813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:33.492860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length6.003115265
Min length6

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I64316.7%
 
A64216.7%
 
C64216.7%
 
T64216.7%
 
V64216.7%
 
E64216.7%
 
N1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter3854100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I64316.7%
 
A64216.7%
 
C64216.7%
 
T64216.7%
 
V64216.7%
 
E64216.7%
 
N1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3854100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I64316.7%
 
A64216.7%
 
C64216.7%
 
T64216.7%
 
V64216.7%
 
E64216.7%
 
N1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3854100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I64316.7%
 
A64216.7%
 
C64216.7%
 
T64216.7%
 
V64216.7%
 
E64216.7%
 
N1< 0.1%
 

WARD
Real number (ℝ≥0)

Distinct51
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.98753894
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Memory size5.1 KiB
2020-12-12T20:18:33.576432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median25
Q340
95-th percentile49
Maximum63
Range62
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.37974336
Coefficient of variation (CV)0.6154965241
Kurtosis-1.285037828
Mean24.98753894
Median Absolute Deviation (MAD)15
Skewness0.05177212359
Sum16042
Variance236.5365059
MonotocityNot monotonic
2020-12-12T20:18:33.659504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
43396.1%
 
5365.6%
 
2314.8%
 
29243.7%
 
18243.7%
 
46223.4%
 
28203.1%
 
20193.0%
 
50193.0%
 
26182.8%
 
10172.6%
 
42162.5%
 
21162.5%
 
4162.5%
 
19152.3%
 
8142.2%
 
39142.2%
 
11142.2%
 
49142.2%
 
32142.2%
 
3142.2%
 
23121.9%
 
25111.7%
 
41111.7%
 
38111.7%
 
Other values (26)18128.2%
 
ValueCountFrequency (%) 
150.8%
 
2314.8%
 
3142.2%
 
4162.5%
 
5365.6%
 
6101.6%
 
791.4%
 
8142.2%
 
9101.6%
 
10172.6%
 
ValueCountFrequency (%) 
6310.2%
 
50193.0%
 
49142.2%
 
4860.9%
 
4791.4%
 
46223.4%
 
4591.4%
 
44101.6%
 
43396.1%
 
42162.5%
 

COMMUNITY AREA
Real number (ℝ≥0)

Distinct76
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.21962617
Minimum0
Maximum77
Zeros2
Zeros (%)0.3%
Memory size5.1 KiB
2020-12-12T20:18:33.750582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median31
Q350
95-th percentile71
Maximum77
Range77
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.27358021
Coefficient of variation (CV)0.6704946076
Kurtosis-1.056160009
Mean33.21962617
Median Absolute Deviation (MAD)18
Skewness0.3030737521
Sum21327
Variance496.1123755
MonotocityNot monotonic
2020-12-12T20:18:33.835655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7436.7%
 
33345.3%
 
24264.0%
 
25264.0%
 
6213.3%
 
42203.1%
 
2193.0%
 
66182.8%
 
3182.8%
 
41152.3%
 
27152.3%
 
16132.0%
 
40132.0%
 
49121.9%
 
52121.9%
 
1111.7%
 
15111.7%
 
61101.6%
 
31101.6%
 
22101.6%
 
71101.6%
 
6991.4%
 
491.4%
 
4391.4%
 
3281.2%
 
Other values (51)24037.4%
 
ValueCountFrequency (%) 
020.3%
 
1111.7%
 
2193.0%
 
3182.8%
 
491.4%
 
540.6%
 
6213.3%
 
7436.7%
 
850.8%
 
950.8%
 
ValueCountFrequency (%) 
7771.1%
 
7540.6%
 
7450.8%
 
7371.1%
 
7271.1%
 
71101.6%
 
7040.6%
 
6991.4%
 
6881.2%
 
6760.9%
 

REGION
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
N
226 
S
222 
C
194 
ValueCountFrequency (%) 
N22635.2%
 
S22234.6%
 
C19430.2%
 
2020-12-12T20:18:33.924232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:18:33.973274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:34.023817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N22635.2%
 
S22234.6%
 
C19430.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter642100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N22635.2%
 
S22234.6%
 
C19430.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin642100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N22635.2%
 
S22234.6%
 
C19430.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII642100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N22635.2%
 
S22234.6%
 
C19430.2%
 

BUILDING NAME
Categorical

HIGH CARDINALITY

Distinct94
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
490 
LINCOLN PARK
 
38
AVALON PARK
 
4
SOUTH SHORE CULTURAL CENTER
 
3
JACKSON PARK
 
3
Other values (89)
104 
ValueCountFrequency (%) 
49076.3%
 
LINCOLN PARK385.9%
 
AVALON PARK40.6%
 
SOUTH SHORE CULTURAL CENTER30.5%
 
JACKSON PARK30.5%
 
GATELY PARK30.5%
 
HAMILTON PARK30.5%
 
PORTAGE PARK20.3%
 
Trumbull Park20.3%
 
WARREN PARK20.3%
 
RIIS PARK20.3%
 
KILBOURN PARK20.3%
 
CALUMET PARK20.3%
 
LOYOLA PARK20.3%
 
RIDGE PARK20.3%
 
OAKDALE PARK20.3%
 
RIVER PARK20.3%
 
INDIAN BOUNDARY PARK20.3%
 
TARKINGTON SCHOLASTIC ACADEMY10.2%
 
DU SABLE MUSEUM OF AFRICAN AMERICAN HISTORY10.2%
 
ECKHART PARK FIELD HOUSE10.2%
 
HOLSTEIN PARK10.2%
 
FULLER PARK (BATHHOUSE)10.2%
 
WILDWOOD PARK10.2%
 
State of Illinois Dept of Military Affairs10.2%
 
Other values (69)6910.7%
 
2020-12-12T20:18:34.107389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique76 ?
Unique (%)11.8%
2020-12-12T20:18:34.192462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length1
Mean length4.795950156
Min length1

Overview of Unicode Properties

Unique unicode characters50
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
74724.3%
 
A2568.3%
 
R2197.1%
 
L1795.8%
 
N1765.7%
 
E1595.2%
 
O1514.9%
 
P1394.5%
 
K1334.3%
 
I1304.2%
 
C973.2%
 
T923.0%
 
S882.9%
 
U732.4%
 
H652.1%
 
M622.0%
 
D521.7%
 
G280.9%
 
F270.9%
 
Y240.8%
 
B230.7%
 
V170.6%
 
l120.4%
 
a110.4%
 
r100.3%
 
Other values (25)1093.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter221271.8%
 
Space Separator74724.3%
 
Lowercase Letter1023.3%
 
Open Punctuation50.2%
 
Close Punctuation50.2%
 
Other Punctuation50.2%
 
Dash Punctuation30.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
747100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A25611.6%
 
R2199.9%
 
L1798.1%
 
N1768.0%
 
E1597.2%
 
O1516.8%
 
P1396.3%
 
K1336.0%
 
I1305.9%
 
C974.4%
 
T924.2%
 
S884.0%
 
U733.3%
 
H652.9%
 
M622.8%
 
D522.4%
 
G281.3%
 
F271.2%
 
Y241.1%
 
B231.0%
 
V170.8%
 
W100.5%
 
J60.3%
 
Q30.1%
 
X20.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l1211.8%
 
a1110.8%
 
r109.8%
 
u87.8%
 
t87.8%
 
e87.8%
 
o76.9%
 
i65.9%
 
b54.9%
 
k54.9%
 
s54.9%
 
m43.9%
 
f43.9%
 
d43.9%
 
y22.0%
 
n22.0%
 
p11.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(5100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)5100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'240.0%
 
.240.0%
 
?120.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin231475.2%
 
Common76524.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
74797.6%
 
(50.7%
 
)50.7%
 
-30.4%
 
'20.3%
 
.20.3%
 
?10.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A25611.1%
 
R2199.5%
 
L1797.7%
 
N1767.6%
 
E1596.9%
 
O1516.5%
 
P1396.0%
 
K1335.7%
 
I1305.6%
 
C974.2%
 
T924.0%
 
S883.8%
 
U733.2%
 
H652.8%
 
M622.7%
 
D522.2%
 
G281.2%
 
F271.2%
 
Y241.0%
 
B231.0%
 
V170.7%
 
l120.5%
 
a110.5%
 
r100.4%
 
W100.4%
 
Other values (18)813.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3079100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
74724.3%
 
A2568.3%
 
R2197.1%
 
L1795.8%
 
N1765.7%
 
E1595.2%
 
O1514.9%
 
P1394.5%
 
K1334.3%
 
I1304.2%
 
C973.2%
 
T923.0%
 
S882.9%
 
U732.4%
 
H652.1%
 
M622.0%
 
D521.7%
 
G280.9%
 
F270.9%
 
Y240.8%
 
B230.7%
 
V170.6%
 
l120.4%
 
a110.4%
 
r100.3%
 
Other values (25)1093.5%
 

DEMOLISHED
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing642
Missing (%)100.0%
Memory size5.1 KiB

YEAR BUILT
Real number (ℝ≥0)

ZEROS

Distinct35
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.5342679
Minimum0
Maximum2007
Zeros574
Zeros (%)89.4%
Memory size5.1 KiB
2020-12-12T20:18:34.272031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1979
Maximum2007
Range2007
Interquartile range (IQR)0

Descriptive statistics

Standard deviation609.3294385
Coefficient of variation (CV)2.908018076
Kurtosis4.613877538
Mean209.5342679
Median Absolute Deviation (MAD)0
Skewness2.568440946
Sum134521
Variance371282.3647
MonotocityNot monotonic
2020-12-12T20:18:34.351099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
057489.4%
 
2005101.6%
 
2006101.6%
 
197950.8%
 
197450.8%
 
196030.5%
 
197220.3%
 
197620.3%
 
197720.3%
 
200720.3%
 
198720.3%
 
200420.3%
 
197810.2%
 
196110.2%
 
183610.2%
 
190010.2%
 
191510.2%
 
191610.2%
 
192310.2%
 
192910.2%
 
194910.2%
 
195810.2%
 
195910.2%
 
196210.2%
 
198110.2%
 
Other values (10)101.6%
 
ValueCountFrequency (%) 
057489.4%
 
183610.2%
 
190010.2%
 
191510.2%
 
191610.2%
 
192310.2%
 
192910.2%
 
194910.2%
 
195810.2%
 
195910.2%
 
ValueCountFrequency (%) 
200720.3%
 
2006101.6%
 
2005101.6%
 
200420.3%
 
199710.2%
 
199510.2%
 
198720.3%
 
198510.2%
 
198110.2%
 
197950.8%
 

X COORDINATE
Real number (ℝ≥0)

Distinct639
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1165890.757
Minimum1121861.759
Maximum1204524.704
Zeros0
Zeros (%)0.0%
Memory size5.1 KiB
2020-12-12T20:18:34.442177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1121861.759
5-th percentile1135101.806
Q11153728.547
median1167846.99
Q31178428.399
95-th percentile1191732.559
Maximum1204524.704
Range82662.94458
Interquartile range (IQR)24699.85127

Descriptive statistics

Standard deviation17253.32521
Coefficient of variation (CV)0.01479840638
Kurtosis-0.4737272161
Mean1165890.757
Median Absolute Deviation (MAD)12144.48834
Skewness-0.2208753623
Sum748501865.8
Variance297677230.8
MonotocityNot monotonic
2020-12-12T20:18:34.531254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1164406.23720.3%
 
1150155.20920.3%
 
1138969.37220.3%
 
1174720.74610.2%
 
1156441.01310.2%
 
1169241.73810.2%
 
1134985.73410.2%
 
1153700.4810.2%
 
1152404.53910.2%
 
1174851.1910.2%
 
1180188.8610.2%
 
1156837.15510.2%
 
1198099.00710.2%
 
1181364.27610.2%
 
1201667.47910.2%
 
1160078.61310.2%
 
1133401.85510.2%
 
1174581.04310.2%
 
1182244.2610.2%
 
1153294.55310.2%
 
1142363.62910.2%
 
1151637.14610.2%
 
1167774.68210.2%
 
1170950.94110.2%
 
1134903.43410.2%
 
Other values (614)61495.6%
 
ValueCountFrequency (%) 
1121861.75910.2%
 
1121993.83210.2%
 
1124420.48810.2%
 
1125099.01110.2%
 
1125237.83610.2%
 
1125249.24110.2%
 
1125307.03410.2%
 
1127215.25410.2%
 
1129454.10810.2%
 
1129561.76410.2%
 
ValueCountFrequency (%) 
1204524.70410.2%
 
1204517.11510.2%
 
1204488.49210.2%
 
1204151.21410.2%
 
1203411.15310.2%
 
1203171.92710.2%
 
1202861.08510.2%
 
1202647.54810.2%
 
1201667.47910.2%
 
1201408.89610.2%
 

Y COORDINATE
Real number (ℝ≥0)

Distinct639
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1891367.954
Minimum1817195.14
Maximum1950435.826
Zeros0
Zeros (%)0.0%
Memory size5.1 KiB
2020-12-12T20:18:34.620831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1817195.14
5-th percentile1836304.906
Q11863620.881
median1896371.399
Q31916897.021
95-th percentile1942744.599
Maximum1950435.826
Range133240.6863
Interquartile range (IQR)53276.14041

Descriptive statistics

Standard deviation33259.49076
Coefficient of variation (CV)0.01758488648
Kurtosis-1.037250232
Mean1891367.954
Median Absolute Deviation (MAD)28706.64354
Skewness-0.16520602
Sum1214258227
Variance1106193726
MonotocityNot monotonic
2020-12-12T20:18:34.716914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1897215.51520.3%
 
1913272.8920.3%
 
1891125.31620.3%
 
1914602.53910.2%
 
1893205.73910.2%
 
1915030.76310.2%
 
1922431.5410.2%
 
1891675.09710.2%
 
1899009.8910.2%
 
1859783.01710.2%
 
1897968.6810.2%
 
1858425.68610.2%
 
1905952.39910.2%
 
1944515.88710.2%
 
1916610.03910.2%
 
1939385.7310.2%
 
1898498.23710.2%
 
1933584.73610.2%
 
1874545.95210.2%
 
1933569.84410.2%
 
1861535.47310.2%
 
1930883.00310.2%
 
1845090.71110.2%
 
1920396.81910.2%
 
1880096.21110.2%
 
Other values (614)61495.6%
 
ValueCountFrequency (%) 
1817195.1410.2%
 
1817677.77910.2%
 
1818472.65210.2%
 
1818701.89310.2%
 
1823159.53510.2%
 
1824053.67610.2%
 
1826688.53810.2%
 
1827100.46410.2%
 
1828792.91510.2%
 
1829081.56310.2%
 
ValueCountFrequency (%) 
1950435.82610.2%
 
1948847.68310.2%
 
1948802.39310.2%
 
1948103.53510.2%
 
1948080.19110.2%
 
1947276.94210.2%
 
1947194.82510.2%
 
1946966.69610.2%
 
1946966.18610.2%
 
1946944.22910.2%
 

LOCATION
Categorical

HIGH CARDINALITY
UNIFORM

Distinct639
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
(41.874077, -87.76524)
 
2
(41.917929, -87.723751)
 
2
(41.856865, -87.67202)
 
2
(41.831721, -87.654505)
 
1
(41.948427, -87.638757)
 
1
Other values (634)
634 
ValueCountFrequency (%) 
(41.874077, -87.76524)20.3%
 
(41.917929, -87.723751)20.3%
 
(41.856865, -87.67202)20.3%
 
(41.831721, -87.654505)10.2%
 
(41.948427, -87.638757)10.2%
 
(41.927138, -87.630683)10.2%
 
(41.763044, -87.639014)10.2%
 
(41.869034, -87.620066)10.2%
 
(41.79843, -87.592804)10.2%
 
(41.72296, -87.533312)10.2%
 
(41.793159, -87.68497)10.2%
 
(41.855026, -87.653937)10.2%
 
(42.008977, -87.692575)10.2%
 
(41.718253, -87.667605)10.2%
 
(41.858627, -87.700126)10.2%
 
(41.691913, -87.61642)10.2%
 
(41.999125, -87.67007)10.2%
 
(41.777373, -87.571379)10.2%
 
(41.919135, -87.633937)10.2%
 
(41.999382, -87.815155)10.2%
 
(41.905476, -87.662961)10.2%
 
(41.968329, -87.762759)10.2%
 
(41.953673, -87.691313)10.2%
 
(41.823731, -87.682457)10.2%
 
(41.820053, -87.59566)10.2%
 
Other values (614)61495.6%
 
2020-12-12T20:18:34.821504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique636 ?
Unique (%)99.1%
2020-12-12T20:18:34.905576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length23
Mean length22.7834891
Min length20

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories6 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
7166111.4%
 
814129.7%
 
.12848.8%
 
412748.7%
 
112728.7%
 
610647.3%
 
98756.0%
 
56844.7%
 
36814.7%
 
26654.5%
 
(6424.4%
 
,6424.4%
 
6424.4%
 
-6424.4%
 
)6424.4%
 
05453.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1013369.3%
 
Other Punctuation192613.2%
 
Open Punctuation6424.4%
 
Space Separator6424.4%
 
Dash Punctuation6424.4%
 
Close Punctuation6424.4%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(642100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
7166116.4%
 
8141213.9%
 
4127412.6%
 
1127212.6%
 
6106410.5%
 
98758.6%
 
56846.8%
 
36816.7%
 
26656.6%
 
05455.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.128466.7%
 
,64233.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
642100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-642100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)642100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common14627100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
7166111.4%
 
814129.7%
 
.12848.8%
 
412748.7%
 
112728.7%
 
610647.3%
 
98756.0%
 
56844.7%
 
36814.7%
 
26654.5%
 
(6424.4%
 
,6424.4%
 
6424.4%
 
-6424.4%
 
)6424.4%
 
05453.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII14627100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
7166111.4%
 
814129.7%
 
.12848.8%
 
412748.7%
 
112728.7%
 
610647.3%
 
98756.0%
 
56844.7%
 
36814.7%
 
26654.5%
 
(6424.4%
 
,6424.4%
 
6424.4%
 
-6424.4%
 
)6424.4%
 
05453.7%
 

Location 1
Categorical

HIGH CARDINALITY
UNIFORM

Distinct639
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
(41.874077, -87.76524)
 
2
(41.917929, -87.723751)
 
2
(41.856865, -87.67202)
 
2
(41.831721, -87.654505)
 
1
(41.948427, -87.638757)
 
1
Other values (634)
634 
ValueCountFrequency (%) 
(41.874077, -87.76524)20.3%
 
(41.917929, -87.723751)20.3%
 
(41.856865, -87.67202)20.3%
 
(41.831721, -87.654505)10.2%
 
(41.948427, -87.638757)10.2%
 
(41.927138, -87.630683)10.2%
 
(41.763044, -87.639014)10.2%
 
(41.869034, -87.620066)10.2%
 
(41.79843, -87.592804)10.2%
 
(41.72296, -87.533312)10.2%
 
(41.793159, -87.68497)10.2%
 
(41.855026, -87.653937)10.2%
 
(42.008977, -87.692575)10.2%
 
(41.718253, -87.667605)10.2%
 
(41.858627, -87.700126)10.2%
 
(41.691913, -87.61642)10.2%
 
(41.999125, -87.67007)10.2%
 
(41.777373, -87.571379)10.2%
 
(41.919135, -87.633937)10.2%
 
(41.999382, -87.815155)10.2%
 
(41.905476, -87.662961)10.2%
 
(41.968329, -87.762759)10.2%
 
(41.953673, -87.691313)10.2%
 
(41.823731, -87.682457)10.2%
 
(41.820053, -87.59566)10.2%
 
Other values (614)61495.6%
 
2020-12-12T20:18:34.995153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique636 ?
Unique (%)99.1%
2020-12-12T20:18:35.077724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length23
Mean length22.7834891
Min length20

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories6 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
7166111.4%
 
814129.7%
 
.12848.8%
 
412748.7%
 
112728.7%
 
610647.3%
 
98756.0%
 
56844.7%
 
36814.7%
 
26654.5%
 
(6424.4%
 
,6424.4%
 
6424.4%
 
-6424.4%
 
)6424.4%
 
05453.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1013369.3%
 
Other Punctuation192613.2%
 
Open Punctuation6424.4%
 
Space Separator6424.4%
 
Dash Punctuation6424.4%
 
Close Punctuation6424.4%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(642100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
7166116.4%
 
8141213.9%
 
4127412.6%
 
1127212.6%
 
6106410.5%
 
98758.6%
 
56846.8%
 
36816.7%
 
26656.6%
 
05455.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.128466.7%
 
,64233.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
642100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-642100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)642100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common14627100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
7166111.4%
 
814129.7%
 
.12848.8%
 
412748.7%
 
112728.7%
 
610647.3%
 
98756.0%
 
56844.7%
 
36814.7%
 
26654.5%
 
(6424.4%
 
,6424.4%
 
6424.4%
 
-6424.4%
 
)6424.4%
 
05453.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII14627100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
7166111.4%
 
814129.7%
 
.12848.8%
 
412748.7%
 
112728.7%
 
610647.3%
 
98756.0%
 
56844.7%
 
36814.7%
 
26654.5%
 
(6424.4%
 
,6424.4%
 
6424.4%
 
-6424.4%
 
)6424.4%
 
05453.7%
 

Interactions

2020-12-12T20:18:29.311762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.393833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.477405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.564480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.651555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.732624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.817197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.899768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:29.980338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.066412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.151986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.228551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.308620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.390691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.475764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.561838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.647412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.728482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.813555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.895625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:30.977696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.063770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.150344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.230914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.315487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.391052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.471621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.551690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.630257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.704321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.781388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.861957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:31.941525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:32.024597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:32.112673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:32.191240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T20:18:35.145282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T20:18:35.264385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T20:18:35.382987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T20:18:35.508595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T20:18:35.630200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T20:18:32.357383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:32.533535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:18:32.615105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

PARKPARK NUMBERBUILDING TYPEBUILDING STATUSWARDCOMMUNITY AREAREGIONBUILDING NAMEDEMOLISHEDYEAR BUILTX COORDINATEY COORDINATELOCATIONLocation 1
0REVERE (PAUL)185FIELDHOUSE B1ACTIVE475NNaN01.158687e+061.926350e+06(41.953644, -87.692044)(41.953644, -87.692044)
1RIDGE175UTILITY STRUCTUREACTIVE1972SNaN01.166000e+061.840626e+06(41.718253, -87.667605)(41.718253, -87.667605)
2RIDGE175FIELDHOUSE A2ACTIVE1972SRIDGE PARKNaN01.165817e+061.840063e+06(41.716714, -87.668292)(41.716714, -87.668292)
3RIDGE175FIELDHOUSE A2ACTIVE1972SRIDGE PARKNaN01.165956e+061.840740e+06(41.718569, -87.667763)(41.718569, -87.667763)
4RIIS (JACOB)123FIELDHOUSE A3ACTIVE2919NRIIS PARKNaN01.135220e+061.915451e+06(41.924184, -87.778574)(41.924184, -87.778574)
5RIIS (JACOB)123UNIDENTIFIEDACTIVE2919NNaN01.134393e+061.916301e+06(41.926532, -87.781594)(41.926532, -87.781594)
6RIIS (JACOB)123UNIDENTIFIEDACTIVE2919NNaN01.134901e+061.916438e+06(41.926898, -87.779722)(41.926898, -87.779722)
7RIIS (JACOB)123POOL BUILDINGACTIVE2919NNaN01.134904e+061.915422e+06(41.92411, -87.779736)(41.92411, -87.779736)
8RIIS (JACOB)123FIELDHOUSE A3ACTIVE2919NRIIS PARKNaN01.134986e+061.915667e+06(41.924781, -87.779429)(41.924781, -87.779429)
9RIIS (JACOB)123MAINTENANCEACTIVE2919NNaN01.134058e+061.915421e+06(41.924122, -87.782845)(41.924122, -87.782845)

Last rows

PARKPARK NUMBERBUILDING TYPEBUILDING STATUSWARDCOMMUNITY AREAREGIONBUILDING NAMEDEMOLISHEDYEAR BUILTX COORDINATEY COORDINATELOCATIONLocation 1
632POTTAWATTOMIE166FIELDHOUSE A3ACTIVE491NNaN01.162299e+061.948802e+06(42.015178, -87.678136)(42.015178, -87.678136)
633PULASKI (CASIMER)217POOL BUILDINGACTIVE3224CNaN01.166648e+061.908839e+06(41.905424, -87.663284)(41.905424, -87.663284)
634PULASKI (CASIMER)217POOL BUILDINGACTIVE3224CNaN01.166549e+061.908967e+06(41.905778, -87.663643)(41.905778, -87.663643)
635PULASKI (CASIMER)217FIELDHOUSE A1ACTIVE3224CNaN01.166636e+061.909317e+06(41.906737, -87.663314)(41.906737, -87.663314)
636PULASKI (CASIMER)217SHELTERACTIVE3224CNaN01.166736e+061.908858e+06(41.905476, -87.662961)(41.905476, -87.662961)
637RAINBOW BEACH1001FIELDHOUSE B1ACTIVE743SNaN20051.198448e+061.855355e+06(41.757921, -87.548268)(41.757921, -87.548268)
638RAINBOW BEACH1001UNIDENTIFIEDACTIVE743SRAINBOW BEACHNaN01.196785e+061.855837e+06(41.759285, -87.554348)(41.759285, -87.554348)
639RAINBOW BEACH1001FIELDHOUSE BHACTIVE743SNaN20051.198133e+061.855513e+06(41.758363, -87.549419)(41.758363, -87.549419)
640RAINBOW BEACH1001UTILITY STRUCTUREACTIVE743SNaN01.197332e+061.855686e+06(41.758858, -87.552349)(41.758858, -87.552349)
641REVERE (PAUL)185POOL BUILDINGACTIVE475NNaN01.158886e+061.926363e+06(41.953673, -87.691313)(41.953673, -87.691313)